Bayesian Estimation of Clustered Dependence Structures in Functional Neuroconnectivity
提出一种基于抽样的贝叶斯聚类方法，用于高维高斯数据的协方差矩阵，通过狄利克雷过程将子矩阵聚类为独立组，实现稀疏性，并在自闭症脑成像数据中识别功能独立的脑区。
We propose a new sampling-based Bayesian clustering approach for covariance matrices of high-dimensional Gaussian outcomes. The key technique is based on a Dirichlet process that clusters covariance sub-matrices to form independent groups of outcomes, thereby naturally inducing sparsity in the covariance matrix. A new split-merge algorithm is employed to achieve convergence of the Markov chain that is shown empirically to recover both equally sized and Dirichlet partitions with high accuracy. We investigate the empirical performance of the proposed method through extensive simulations. Finally, the proposed approach is used to group regions of interest into functionally independent groups in the Autism Brain Imaging Data Exchange participants with autism spectrum and attention-deficit/hyperactivity disorders. Supplementary materials for this article are available online.